Inferoa AI harness claimed 90% cache savings. We ran it and measured 97.8% (zozo123.github.io)

Inferoa has announced the release of an inference-native agent harness that significantly optimizes long-horizon coding tasks by achieving a remarkable 97.8% prefix cache hit rate during simulations. By treating inference mechanics—including context shape and model routing—as foundational design principles, Inferoa positions itself as a pivotal tool for AI developers. This achievement not only surpasses the originally claimed 90% cache savings but also demonstrates the potential for reducing operational costs in AI coding workflows. The implications for the AI and machine learning community are profound. Inferoa's framework employs a structured approach that maintains byte-stable prefixes, effectively minimizing token usage in coding tasks. This enables agents to manage complex repositories with less overhead, streamlining processes such as debugging and testing. Through a simulation that showcased its application in real coding scenarios, Inferoa proves its ability to execute automated fixes and integrate them back into main branches, reinforcing the utility of intelligent agents in software development while offering substantial cost-efficiency for ongoing AI inference tasks.